We developed a low‑cost, high‑precision monocular visual ranging system based on the You Only Look Once version 5 (YOLOv5) object detection algorithm, addressing the limitations of traditional methods by integrating deep learning with computer vision. The YOLOv5 architecture is optimized through the adoption of the cross stage partial darknet backbone network and the Mosaic data augmentation strategy. The architecture improves vehicle detection accuracy with a mean average precision at intersection over union threshold 0.5 of 0.5719, and the inference speed of 140 frames per second. Multiscale object detection capabilities are further enhanced using an adaptive anchor box generation mechanism. The monocular ranging model was constructed by combining camera calibration parameters with perspective geometry principles. Bounding box information produced by YOLOv5 is mapped to three‑dimensional spatial distances through the relationship between object pixel dimensions and actual physical dimensions, effectively mitigating the scale ambiguity inherent in monocular vision. The experimental results showed that the system maintained average ranging errors within engineering‑acceptable limits under diverse lighting and weather conditions while satisfying real‑time requirements. Compared with traditional geometric ranging benchmarks, the optimized model showed a 12% improvement in mean absolute error and root mean square error with an overall accuracy enhancement of 16.88%. The results confirm that the developed monocular visual ranging model balances cost and performance and is a reliable method for intelligent transportation systems. By functioning as a robust software‑defined virtual sensor, the system offers significant engineering application value and contributes to the advancement of intelligent driving technologies.